# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" test control ops """
import numpy as np
from tests.st.control.cases_register import case_register

from mindspore import dtype as ms
from mindspore import Tensor
from mindspore import context
from mindspore import nn
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.ops import composite as C
from mindspore.ops import operations as P


grad_by_list = C.GradOperation(get_by_list=True)
grad_all = C.GradOperation(get_all=True)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()

        def construct(self, idx, end, x):
            while idx < end:
                part = x[idx, :, :]
                max_num = self.max(part)
                x[idx, :, 0:2] = max_num
                idx = idx + 1
            return x

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(2), dtype=ms.int32)
    input_x = np.array([[[4, 0], [0, 0]],
                        [[0, 4], [0, 0]]]).astype(np.float32)
    x = Tensor(input_x, dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)

    expect_zero = np.array([0], dtype=np.float32)
    expect_two = input_x
    assert np.allclose(graph_output[0].asnumpy(), expect_zero, 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect_zero, 0.0001, 0.0001)
    assert np.allclose(graph_output[2].asnumpy(), expect_two, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_const_param_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.mul = P.Mul()
            self.add = P.Add()

        def construct(self, x, y):
            while x < y:
                z = self.mul(x, x)
                x = self.add(z, 1)
            return x

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor([1.1], dtype=ms.float32)
    end = Tensor([8.0], dtype=ms.float32)
    graph_output = net(idx, end)
    expect_one = np.array([1.14433983e+02], dtype=np.float32)
    expect_two = np.array([0], dtype=np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect_one, 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect_two, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_variable_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.mul = P.Mul()
            self.add = P.Add()

        def construct(self, x, y):
            while x < y:
                z = self.mul(x, x)
                x = self.add(z, y)
            return x

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor([1.1], dtype=ms.float32)
    end = Tensor([8.0], dtype=ms.float32)
    graph_output = net(idx, end)
    expect_one = np.array([2.20000005e+00], dtype=np.float32)
    expect_two = np.array([1.00000000e+00], dtype=np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect_one, 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect_two, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_forward():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                part = x[idx, :, :]
                max_num = self.max(part)
                x[idx, :, 0:2] = max_num
                out = out + x + self.param
                idx = idx + 1
            return out

    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    net = MyWhileNet()
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(2), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    graph_output = net(idx, end, x)
    expect = np.array([[[6, 8], [10, 12]], [[19, 22], [25, 28]]], dtype=np.int32)
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_endless_case():
    """endless case when optimization"""

    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                part = x[idx, :, :]
                out = out + part
                idx = idx + 1
            return out

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(2), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    net = MyWhileNet()
    graph_output = net(idx, end, x)
    expect = np.array([[[4, 6], [8, 10]],
                       [[4, 6], [8, 10]]]).astype(np.float32)
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                part = x[idx, :, :]
                max_num = self.max(part)
                x[idx, :, 0:2] = max_num
                out = out + x + self.param
                idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(2), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    graph_output = net(idx, end, x)
    expect = np.array([[[2, 2], [2, 2]], [[2, 2], [2, 2]]], dtype=np.int32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_forward_with_const_branch():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                if 2 > 1:
                    out = out + self.param
                else:
                    out = out + idx + self.param
                idx = idx + 1
            return out

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = while_net
    graph_output = net(idx, end, x)

    expect = np.array([[[0, 4], [8, 12]],
                       [[16, 20], [24, 28]]]).astype(np.float32)
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_opt_endless():
    """endless during optimization case"""

    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.addn = P.AddN()

        def construct(self, idx, end, x):
            addn1 = self.addn((x, x, x))
            out = addn1
            while idx < end:
                out = self.addn((out, addn1))
                idx = idx + 1
            out = self.addn((out, x))
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)

    expect1 = 0
    expect2 = 0
    expect3 = np.array([[[16, 16], [16, 16]],
                        [[16, 16], [16, 16]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect1, 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect2, 0.0001, 0.0001)
    assert np.allclose(graph_output[2].asnumpy(), expect3, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_no_while_call():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()

        def construct(self, idx, end, x):
            out = self.zero
            if 2 > 1:
                out = out + self.param
            else:
                out = out + idx + self.param
            return out

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = while_net
    graph_output = net(idx, end, x)

    expect = np.array([[[0, 1], [2, 3]],
                       [[4, 5], [6, 7]]]).astype(np.float32)
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_grad_with_const_branch():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                if 2 > 1:
                    out = out + self.param
                else:
                    out = out + idx + self.param
                idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)

    expect = np.array([[[4, 4], [4, 4]],
                       [[4, 4], [4, 4]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_for_while_with_param_grad_with_const_branch():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.start = Tensor(np.array(0), dtype=ms.int32)

        def construct(self, idx, end, x):
            out = self.zero
            for _ in range(0, 2):
                idx = self.start
                while idx < end:
                    if 2 > 1:
                        out = out + self.param
                    else:
                        out = out + idx + self.param
                    idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)

    expect = np.array([[[8, 8], [8, 8]],
                       [[8, 8], [8, 8]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_for_while_with_param_grad_basic():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.start = Tensor(np.array(0), dtype=ms.int32)

        def construct(self, idx, end, x):
            out = self.zero
            for _ in range(0, 2):
                idx = self.start
                while idx < end:
                    out = out + self.param
                    idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)
    expect = np.array([[[8, 8], [8, 8]],
                       [[8, 8], [8, 8]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_for_while_with_param_grad_normal():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.start = Tensor(np.array(0), dtype=ms.int32)

        def construct(self, idx, end, x):
            out = x
            for _ in range(0, 2):
                idx = self.start
                while idx < end:
                    out = out + self.param
                    idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)
    expect = np.array([[[8, 8], [8, 8]],
                       [[8, 8], [8, 8]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_basic_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)
    expect = np.array([[[4, 4], [4, 4]],
                       [[4, 4], [4, 4]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_basic_grad_mul():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.ones(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out * self.param
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)
    expect = np.array([[[1, 4], [13, 28]],
                       [[49, 76], [109, 148]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_basic_grad_two():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param + self.weight
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)

    expect1 = np.array([[[4, 4], [4, 4]],
                        [[4, 4], [4, 4]]]).astype(np.float32)
    expect2 = np.array([[[3, 3], [3, 3]],
                        [[3, 3], [3, 3]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect1, 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect2, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_with_param_basic_grad_three():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
            self.key = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="key")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param + self.weight + self.key
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)
    expect1 = np.array([[[4, 4], [4, 4]],
                        [[4, 4], [4, 4]]]).astype(np.float32)
    expect2 = np.array([[[3, 3], [3, 3]],
                        [[3, 3], [3, 3]]]).astype(np.float32)
    expect3 = np.array([[[3, 3], [3, 3]],
                        [[3, 3], [3, 3]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect1, 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect2, 0.0001, 0.0001)
    assert np.allclose(graph_output[2].asnumpy(), expect3, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_while_if_with_param_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                if self.max(out) < self.max(x):
                    out = out + self.param * 2
                else:
                    out = out + self.param
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)
    expect = np.array([[[5, 5], [5, 5]],
                       [[5, 5], [5, 5]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_while_with_param_grad_not_enter_while():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(2, ms.float32), name="weight")
            self.zero = Tensor(0, ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param * 3
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    idx = Tensor(np.array(3), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(2, dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    while_net = MyWhileNet()
    net = GradNet(while_net)
    graph_output = net(idx, end, x)

    assert np.allclose(graph_output[0].asnumpy(), 1, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_with_param_if_by_if_forward():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param
            else:
                out = out + x
            if a == b:
                out = out + x * 3 + self.param
            else:
                out = out + x * 2
            return out

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)
    expect = np.array([[[3, 4], [5, 6]],
                       [[7, 8], [9, 10]]]).astype(np.float32)
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_with_param_if_by_if_grad_inputs():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param * 4
            if a == b:
                out = out + x * 3 + self.param * 3
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    graph_output = net(idx, end, x)
    expect1 = Tensor(np.array(0), dtype=ms.int32)
    expect2 = Tensor(np.array(0), dtype=ms.int32)
    expect3 = np.array([[[3, 3], [3, 3]],
                        [[3, 3], [3, 3]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect1.asnumpy(), 0.0001, 0.0001)
    assert np.allclose(graph_output[1].asnumpy(), expect2.asnumpy(), 0.0001, 0.0001)
    assert np.allclose(graph_output[2].asnumpy(), expect3, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_with_param_if_by_if_grad_parameter():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param * 2
            if a == b:
                out = out + x * 3 + self.param
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            return grad_by_list(self.net, self.weights)(*inputs)

    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(2), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    graph_output = net(idx, end, x)

    expect = np.array([[[2, 2], [2, 2]],
                       [[2, 2], [2, 2]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_with_param_if_by_if_grad_param_excute_null():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param * 2
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            return grad_by_list(self.net, self.weights)(*inputs)

    idx = Tensor(np.array(4), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    graph_output = net(idx, end, x)

    expect = np.array([[[0, 0], [0, 0]],
                       [[0, 0], [0, 0]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_if_by_if_return_inside_grad():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                return out + x + self.param
            if a == b:
                return out + self.param * 2
            return out + self.param * 3

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            return grad_by_list(self.net, self.weights)(*inputs)

    idx = Tensor(np.array(1), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    graph_output = net(idx, end, x)

    expect = np.array([[[3, 3], [3, 3]],
                       [[3, 3], [3, 3]]]).astype(np.float32)
    assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_if_by_if_forward():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            if a < b:
                a = self.add(a, b)
            else:
                a = self.sub(a, b)
            if a == x:
                a = self.mul(a, b)
            else:
                a = self.div(a, b)
            if b == x:
                b = self.add(a, b)
            else:
                b = self.add(a, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(4), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)
    expect = 19.11111
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_if_by_if_forward_control_tuple_switch():
    """tuple_get from  switch op will generate new switch inside to eliminate tuple_get"""

    class Branch3Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            if b == x:
                b = self.add(a, b)
            else:
                b = self.add(a, x)
            return a, b, x

    class Branch2Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch3Net()

        def construct(self, a, b, x):
            if a == x:
                a = self.mul(a, b)
            else:
                a = self.div(a, b)
            return self.net(a, b, x)

    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch2Net()

        def construct(self, a, b, x):
            if a < b:
                a = self.add(a, b)
            else:
                a = self.sub(a, b)
            a, b, x = self.net(a, b, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)
    expect = 4.444444
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
@case_register.target_gpu
def test_if_by_if_forward_control_inside_net():
    class Branch3Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            if b == x:
                b = self.add(a, b)
            else:
                b = self.add(a, x)
            a = a * b
            out = a + b + x
            return out

    class Branch2Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch3Net()

        def construct(self, a, b, x):
            if a == x:
                a = self.mul(a, b)
            else:
                a = self.div(a, b)
            return self.net(a, b, x)

    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch2Net()

        def construct(self, a, b, x):
            if a < b:
                a = self.add(a, b)
            else:
                a = self.sub(a, b)
            out = self.net(a, b, x)
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)
    expect = 4.444444
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_if_by_if_forward_use_namespace():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            if a < b:
                a = P.Add()(a, b)
            else:
                a = P.Sub()(a, b)
            if a == x:
                a = P.Mul()(a, b)
            else:
                a = P.RealDiv()(a, b)
            if b == x:
                b = P.Add()(a, b)
            else:
                b = P.Add()(a, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)
    expect = 4.444444
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_if_by_if_forward_use_global_op():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            add = P.Add()
            sub = P.Sub()
            mul = P.Mul()
            div = P.RealDiv()
            if a < b:
                a = add(a, b)
            else:
                a = sub(a, b)
            if a == x:
                a = mul(a, b)
            else:
                a = div(a, b)
            if b == x:
                b = add(a, b)
            else:
                b = add(a, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)

    expect = 4.444444
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_for_with_if_by_if_forward():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()

        def construct(self, a, b, x):
            for _ in range(0, 4):
                if a < b:
                    a = self.add(a, b)
                else:
                    b = self.sub(b, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)

    expect = 18.0
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_for_with_if_by_if_forward_namespace():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            for _ in range(0, 6):
                if a < b:
                    a = P.Add()(a, b)
                else:
                    b = P.Sub()(b, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)

    expect = 18.0
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_if_by_if_forward_const_branch_inner():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            add = P.Add()
            sub = P.Sub()
            mul = P.Mul()
            div = P.RealDiv()
            if a < b:
                a = add(a, b)
            else:
                a = sub(a, b)
            if 2 > 1:
                a = mul(a, b)
            else:
                a = div(a, b)
            if b == x:
                b = add(a, b)
            else:
                b = add(a, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)

    expect = 240.0
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_ascend
def test_if_by_if_forward_all_const_branch():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            add = P.Add()
            sub = P.Sub()
            mul = P.Mul()
            div = P.RealDiv()
            if 2 < 12:
                a = add(a, b)
            else:
                a = sub(a, b)
            if 2 > 1:
                a = mul(a, b)
            else:
                a = div(a, b)
            if 2 == 1:
                b = add(a, b)
            else:
                b = add(a, x)
            a = a * b
            out = a + b + x
            return out

    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    # graph mode
    context.set_context(mode=context.GRAPH_MODE)
    if_net = MyIfByIfNet()
    net = if_net
    graph_output = net(idx, end, x)

    expect = 240.0
    assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)


@case_register.level1
@case_register.target_gpu
def test_if_const_grad():
    class MyNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()

        def construct(self, *inputs):
            out = self.add(*inputs)
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            a = 1
            b = 2
            if a > 0:
                b = 1
            a += b
            return grad_by_list(self.net, self.weights)(*inputs)

    context.set_context(mode=context.GRAPH_MODE)
    my_net = MyNet()
    net = GradNet(my_net)
    a = Tensor(np.array(0), dtype=ms.int32)
    b = Tensor(np.array(1), dtype=ms.int32)
    net(a, b)


@case_register.level1
@case_register.target_gpu
def test_if_by_if_const_grad():
    class MyNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()

        def construct(self, *inputs):
            out = self.add(*inputs)
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            a = 1
            b = 2
            if a > 0:
                b = 1
            if a < 0:
                b = 0
            if a == 0:
                b = 3
            a += b
            return grad_by_list(self.net, self.weights)(*inputs)

    context.set_context(mode=context.GRAPH_MODE)
    my_net = MyNet()
    net = GradNet(my_net)
    a = Tensor(np.array(0), dtype=ms.int32)
    b = Tensor(np.array(1), dtype=ms.int32)
    net(a, b)


@case_register.level1
@case_register.target_gpu
def test_while_const_grad():
    class MyNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()

        def construct(self, *inputs):
            out = self.add(*inputs)
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            a = 1
            while a > 1:
                a = a - 1
            return grad_by_list(self.net, self.weights)(*inputs)

    context.set_context(mode=context.GRAPH_MODE)
    my_net = MyNet()
    net = GradNet(my_net)
    a = Tensor(np.array(0), dtype=ms.int32)
    b = Tensor(np.array(1), dtype=ms.int32)
    net(a, b)


@case_register.level1
@case_register.target_gpu
def test_if_by_while_const_grad():
    class MyNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.Add()

        def construct(self, *inputs):
            out = self.add(*inputs)
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        def construct(self, *inputs):
            a = 1
            b = 2
            if a > 0:
                b = 0
            while a > 1:
                a = a - 1
            a += b
            return grad_by_list(self.net, self.weights)(*inputs)

    context.set_context(mode=context.GRAPH_MODE)
    my_net = MyNet()
    net = GradNet(my_net)
    a = Tensor(np.array(0), dtype=ms.int32)
    b = Tensor(np.array(1), dtype=ms.int32)
    net(a, b)
